# Deep Motion Network for Freehand 3D Ultrasound Reconstruction

arXiv: https://arxiv.org/abs/2207.00177 · HTML: https://mlsanity.com/paper/2207.00177/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 1 Jul 2022.

## Builds on (most cited first, 3 of 3)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Self Context and Shape Prior for Sensorless Freehand 3D Ultrasound Reconstruction](https://mlsanity.com/paper/2108.00274/) (2021)
- [Sensorless Freehand 3D Ultrasound Reconstruction via Deep Contextual Learning](https://mlsanity.com/paper/2006.07694/) (2020)

## Built on by (most cited first, 14 of 14)

- [Trackerless freehand ultrasound with sequence modelling and auxiliary transformation over past and future frames](https://mlsanity.com/paper/2211.04867/) (2022)
- [Long-term Dependency for 3D Reconstruction of Freehand Ultrasound Without External Tracker](https://mlsanity.com/paper/2310.10248/) (2023)
- [Multi-IMU with Online Self-Consistency for Freehand 3D Ultrasound Reconstruction](https://mlsanity.com/paper/2306.16197/) (2023)
- [Fine-grained Context and Multi-modal Alignment for Freehand 3D Ultrasound Reconstruction](https://mlsanity.com/paper/2407.04242/) (2024)
- [TUS-REC2024: A Challenge to Reconstruct 3D Freehand Ultrasound Without External Tracker](https://mlsanity.com/paper/2506.21765/) (2025)
- [Nonrigid Reconstruction of Freehand Ultrasound without a Tracker](https://mlsanity.com/paper/2407.05767/) (2024)
- [MoNetV2: Enhanced Motion Network for Freehand 3D Ultrasound Reconstruction](https://mlsanity.com/paper/2506.15835/) (2025)
- [Enhancing Free-hand 3D Photoacoustic and Ultrasound Reconstruction using Deep Learning](https://mlsanity.com/paper/2502.03505/) (2025)
- [DualTrack: Sensorless 3D Ultrasound needs Local and Global Context](https://mlsanity.com/paper/2509.09530/) (2025)
- [Privileged Anatomical and Protocol Discrimination in Trackerless 3D Ultrasound Reconstruction](https://mlsanity.com/paper/2308.10293/) (2023)
- [Optimization-Based Calibration for Intravascular Ultrasound Volume Reconstruction](https://mlsanity.com/paper/2508.20605/) (2025)
- [UltraGS: Real-Time Physically-Decoupled Gaussian Splatting for Ultrasound Novel View Synthesis](https://mlsanity.com/paper/2511.07743/) (2025)
- [MLRecon: Robust Markerless Freehand 3D Ultrasound Reconstruction via Coarse-to-Fine Pose Estimation](https://mlsanity.com/paper/2603.00990/) (2026)
- [Seeing Globally, Refining Locally: Global Visual Guidance and Local Ultrasound Cues for Robust Freehand 3-D Ultrasound Reconstruction](https://mlsanity.com/paper/2607.12398/) (2026)

## Abstract (arXiv)

Freehand 3D ultrasound (US) has important clinical value due to its low cost and unrestricted field of view. Recently deep learning algorithms have removed its dependence on bulky and expensive external positioning devices. However, improving reconstruction accuracy is still hampered by difficult elevational displacement estimation and large cumulative drift. In this context, we propose a novel deep motion network (MoNet) that integrates images and a lightweight sensor known as the inertial measurement unit (IMU) from a velocity perspective to alleviate the obstacles mentioned above. Our contribution is two-fold. First, we introduce IMU acceleration for the first time to estimate elevational displacements outside the plane. We propose a temporal and multi-branch structure to mine the valuable information of low signal-to-noise ratio (SNR) acceleration. Second, we propose a multi-modal online self-supervised strategy that leverages IMU information as weak labels for adaptive optimization to reduce drift errors and further ameliorate the impacts of acceleration noise. Experiments show that our proposed method achieves the superior reconstruction performance, exceeding state-of-the-art methods across the board.